Repo for my graduate data science machine learning class at UCSD (UC San Diego). This course provides a broad introduction to the practical side of machine-learning and data analysis. The topics covered in this class include topics in supervised learning, such as k-nearest neighbor classifiers, decision trees, boosting and perceptrons, and topics in unsupervised learning, such as k-means, PCA and Gaussian mixture models.

Assignment 2: Sabermetrics, Nearest Neighbor Classification, and Housing Prices using Regression Trees
Assignment 1: Pandas, Bash for Basic ETL, and Decision Trees